A Novel Predictive Multi-Marker Test for the Pre-Surgical Identification of Ovarian Cancer

Abstract

Ovarian cancer remains the most lethal of gynecological malignancies, with the 5-year survival below 50%. Currently there is no simple and effective pre-surgical diagnosis or triage for patients with malignancy, particularly those with early-stage or low-volume tumors. Recently we discovered that CXCL10 can be processed to an inactive form in ovarian cancers and that its measurement has diagnostic significance. In this study we evaluated the addition of processed CXCL10 to a biomarker panel for the discrimination of benign from malignant disease. Multiple biomarkers were measured in retrospectively collected plasma samples (n = 334) from patients diagnosed with benign or malignant disease, and a classifier model was developed using CA125, HE4, Il6 and CXCL10 (active and total). The model provided 95% sensitivity/95% specificity for discrimination of benign from malignant disease. Positive predictive performance exceeded that of “gold standard” scoring systems including CA125, RMI and ROMA% and was independent of menopausal status. In addition, 80% of stage I-II cancers in the cohort were correctly identified using the multi-marker scoring system. Our data suggest the multi-marker panel and associated scoring algorithm provides a useful measurement to assist in pre-surgical diagnosis and triage of patients with suspected ovarian cancer.